## `summarise()` has grouped output by 'sub', 'nquestion', 'round_text'. You can override using the `.groups` argument.
## Joining, by = "sub"
## Joining, by = "sub"
| Prescan Performance | |||
|---|---|---|---|
| sub | correct1 | conf | conf_correct2 |
| 01 | 0.750 | 0.333 | 0.333 |
| 02 | 0.667 | 0.417 | 0.417 |
| 03 | 0.792 | 0.500 | 0.458 |
| 04 | 0.667 | 0.625 | 0.500 |
| 05 | 0.250 | 0.500 | 0.083 |
| 06 | 0.708 | 0.667 | 0.583 |
| 07 | 0.708 | 0.333 | 0.333 |
| 08 | 0.333 | 0.333 | 0.333 |
| 09 | 0.792 | 0.458 | 0.458 |
| 10 | 0.792 | 0.458 | 0.458 |
| 11 | 0.708 | 0.458 | 0.458 |
| 12 | 0.833 | 0.583 | 0.583 |
| 13 | 0.583 | 0.917 | 0.542 |
| 14 | 0.167 | 0.458 | 0.125 |
| 15 | 0.750 | 0.542 | 0.458 |
| 16 | 0.750 | 0.500 | 0.417 |
| 17 | 0.458 | 0.708 | 0.417 |
| 18 | 0.625 | 0.708 | 0.542 |
| 19 | 0.792 | 0.583 | 0.583 |
| 20 | 0.750 | 0.625 | 0.542 |
|
1
accuracy < 0.5 are highlighted
2
confidence correct < 0.25 are highlighted
|
|||
| Scan Performance | |||
|---|---|---|---|
| sub | correct1 | conf | conf_correct2 |
| 01 | 0.925 | 0.025 | 0.025 |
| 02 | 0.850 | 0.750 | 0.725 |
| 03 | 0.650 | 0.225 | 0.175 |
| 04 | 0.950 | 0.950 | 0.925 |
| 05 | 0.175 | 0.300 | 0.125 |
| 06 | 0.775 | 0.725 | 0.675 |
| 07 | 0.750 | 0.000 | 0.000 |
| 08 | 0.475 | 0.725 | 0.450 |
| 09 | 0.975 | 0.575 | 0.575 |
| 10 | 0.875 | 0.625 | 0.625 |
| 11 | 0.900 | 0.800 | 0.775 |
| 12 | 0.800 | 0.900 | 0.750 |
| 13 | 0.900 | 0.925 | 0.850 |
| 14 | 0.375 | 0.700 | 0.350 |
| 15 | 0.225 | 0.250 | 0.175 |
| 16 | 0.300 | 0.300 | 0.125 |
| 17 | 0.875 | 0.550 | 0.550 |
| 18 | 0.750 | 0.600 | 0.600 |
| 19 | 0.975 | 0.950 | 0.925 |
| 20 | 0.650 | 0.475 | 0.425 |
|
1
accuracy < 0.5 are highlighted
2
confidence correct < 0.25 are highlighted
|
|||
| Mean accuracy per subject | |
|---|---|
| sub | m1 |
| 01 | 0.9375 |
| 02 | 0.9375 |
| 03 | 1.0000 |
| 04 | 1.0000 |
| 05 | 0.3125 |
| 06 | 1.0000 |
| 07 | 0.9375 |
| 08 | 1.0000 |
| 09 | 1.0000 |
| 10 | 1.0000 |
| 11 | 1.0000 |
| 12 | 1.0000 |
| 13 | 0.8125 |
| 14 | 0.8125 |
| 15 | 1.0000 |
| 16 | 0.8750 |
| 17 | 1.0000 |
| 18 | 1.0000 |
| 19 | 1.0000 |
| 20 | 1.0000 |
|
1
accuracy < 0.5 are highlighted
|
|
Excluding subject 05, 14, 15, and 16. Should we exclude sub 08?
Notice: sub1/3/7 have very low confidence.
Participant were instructed to answer the expected destination for 3 times during the route: once at Same, once at Overlapping, and once at non-overlapping. They also indicated their confidence towards the choice (sure vs. unsure).
## `summarise()` has grouped output by 'round_text'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'nquestion', 'round'. You can override using the `.groups` argument.
ANVOA for Accuracy:
## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 19 6.9241983 1.644407e-02 * 0.124975005
## 2 nquestion 1 19 76.0754567 4.536688e-08 * 0.633424084
## 3 round:nquestion 1 19 0.9223301 3.489272e-01 0.008496658
ANVOA for Confidence:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 19 3.776025 6.695750e-02 0.05876083
## 2 nquestion 1 19 81.230299 2.731736e-08 * 0.71613992
## 3 round:nquestion 1 19 2.064302 1.670474e-01 0.01029772
ANVOA for high confidence accuracy:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 19 9.545830 6.032885e-03 * 0.10377224
## 2 nquestion 1 19 93.861783 8.744699e-09 * 0.76533294
## 3 round:nquestion 1 19 3.352941 8.281384e-02 0.01893261
t-test for mean (overlapping segment):
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$m
## t = -2.4495, df = 19, p-value = 0.02417
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.27817102 -0.02182898
## sample estimates:
## mean of the differences
## -0.15
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$m
## t = -1.1888, df = 19, p-value = 0.2492
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.20704748 0.05704748
## sample estimates:
## mean of the differences
## -0.075
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$m
## t = -2.6659, df = 19, p-value = 0.01527
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.15619784 -0.01880216
## sample estimates:
## mean of the differences
## -0.0875
t-test for confidence (overlapping segment):
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$conf
## t = 0.27085, df = 19, p-value = 0.7894
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08409559 0.10909559
## sample estimates:
## mean of the differences
## 0.0125
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$conf
## t = -2.4908, df = 19, p-value = 0.02217
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.36806043 -0.03193957
## sample estimates:
## mean of the differences
## -0.2
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$conf
## t = -1.3708, df = 19, p-value = 0.1864
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.18951388 0.03951388
## sample estimates:
## mean of the differences
## -0.075
t-test for high confidence accuracy (overlapping segment):
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$cor_conf
## t = 0, df = 19, p-value = 1
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1073699 0.1073699
## sample estimates:
## mean of the differences
## 0
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$cor_conf
## t = -3.3275, df = 19, p-value = 0.003539
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3665288 -0.0834712
## sample estimates:
## mean of the differences
## -0.225
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$cor_conf
## t = -2.2687, df = 19, p-value = 0.03513
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.216287016 -0.008712984
## sample estimates:
## mean of the differences
## -0.1125
Accuracy per round:
## `summarise()` has grouped output by 'round'. You can override using the `.groups` argument.
Distribution of picture index:
Grouped in 10:
## `summarise()` has grouped output by 'npic_10', 'route'. You can override using the `.groups` argument.
Grouped in 5:
## `summarise()` has grouped output by 'npic_5', 'route'. You can override using the `.groups` argument.
Every picture:
## `summarise()` has grouped output by 'npic', 'route'. You can override using the `.groups` argument.
Average accuracy = 0.9765625
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.